Abstract
Anemia is a worldwide health issue. To diagnose anemia, blood must be drawn to examine the hemoglobin level. The procedure is time-consuming and labor-intensive. The existing Artificial Intelligence (AI)-based anemia detection methods in literature have shortcomings, including, i) specially designed data collection device, ii) manual feature extraction, iii) small data size for training the model, and iv)user’s trust in AI prediction. In this paper, we aim to provide a non-invasive model of anemia detection from visible signs. We trained a CNN model on eye-membrane image data collected from real patients and open image sources. Our model predicts anemic patients with good accuracy at 98%. In addition, we proposed the explainable AI method as a part of the non-invasive diagnosis to enhance the user’s trust in the CNN model’s prediction.
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Acknowledgements
This research is supported in part by collaborative research funding from the National Program Office under National Research Council of Canada’s Artificial Intelligence for Logistics Program.
Also, we cordially thank Dr.Nusrat Jahan, MBBS,BCS (health), PDS code : 141946, Assistant Surgeon at government of the people’s republic of Bangladesh; for supporting the medical background and data scrutinizing of this research .
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Ferdousi, R., Mabruba, N., Laamarti, F., El Saddik, A., Yang, C. (2022). Non-invasive Anemia Detection from Conjunctival Images. In: Berretti, S., Su, GM. (eds) Smart Multimedia. ICSM 2022. Lecture Notes in Computer Science, vol 13497. Springer, Cham. https://doi.org/10.1007/978-3-031-22061-6_14
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DOI: https://doi.org/10.1007/978-3-031-22061-6_14
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